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Technology Acceptance Model 3 and a Research Agenda on Interventions

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ABSTRACT Prior research has provided valuable insights into how and why employees make a decision about the adoption and use of information technologies (ITs) in the workplace. From an organizational point of view, however, the more important issue is how managers make informed decisions about interventions that can lead to greater acceptance and effective utilization of IT. There is limited research in the IT implementation literature that deals with the role of interventions to aid such managerial decision making. Particularly, there is a need to understand how various interventions can influence the known determinants of IT adoption and use. To address this gap in the literature, we draw from the vast body of research on the technology acceptance model (TAM), particularly the work on the determinants of perceived usefulness and perceived ease of use, and: (i) develop a comprehensive nomological network (integrated model) of the determinants of individual level (IT) adoption and use; (ii) empirically test the proposed integrated model; and (iii) present a research agenda focused on potential pre- and postimplementation interventions that can enhance employees' adoption and use of IT. Our findings and research agenda have important implications for managerial decision making on IT implementation in organizations.
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Please cite this article as:
Venkatesh, V. and Bala, H. “Technology Acceptance Model 3 and a Research Agenda on
Interventions,” Decision Sciences (39:2), 2008, 273-315.
https://doi.org/10.1111/j.1540-5915.2008.00192.x
TECHNOLOGY ACCEPTANCE MODEL 3 AND A RESEARCH AGENDA ON
INTERVENTIONS
Viswanath Venkatesh
University of Arkansas
Hillol Bala
Indiana University
vvenkatesh@vvenkatesh.us
This is a pre-publication version and was subject to copyediting and proofing prior to
publication.
Corresponding author.
†† Effective July 1, 2008.
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ABSTRACT
Prior research has provided valuable insights into how and why employees make a decision
about the adoption and use of information technologies (ITs) in the workplace. From an
organizational point of view, however, the more important issues are how managers make
informed decisions about interventions that can lead to greater acceptance and effective
utilization of IT. There is limited research in the IT implementation literature that deals with the
role of interventions to aid such managerial decision making. Particularly, there is a need to
understand how various interventions can influence the known determinants of IT adoption and
use. To address this gap in the literature, we draw from the vast body of research on the
technology acceptance model (TAM), particularly the work on the determinants of perceived
usefulness and perceived ease of use, and: (i) develop a comprehensive nomological network
(integrated model) of the determinants of individual level adoption and use; (ii) empirically test
the proposed integrated model; and (iii) present a research agenda focused on potential pre- and
post-implementation interventions that can enhance employees’ adoption and use of IT. Our
findings and research agenda have important implications for managerial decision making on IT
implementation in organizations.
Subject Areas: Technology Acceptance Model (TAM), Technology Adoption, User Acceptance,
Interventions, Design Characteristics, Training, User Participation, User Involvement,
Management Support, Organizational Support, Peer Support.
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Introduction
While great progress has been made in understanding the determinants of employees’
information technology (IT) adoption and use (Venkatesh, Morris, Davis, & Davis, 2003), trade
press still suggests that low adoption and use of IT by employees are still major barriers to
successful IT implementations in organizations (Gross, 2005; Overby, 2002). As ITs are
becoming increasingly complex and central to organizational operations and managerial decision
making (e.g., enterprise resource planning, supply chain management, customer relationship
management systems), this issue has become even more severe. There are numerous examples of
IT implementation failures in organizations leading to huge financial losses. Two high profile
examples IT implementation failures are Hewlett-Packard’s (HP) failure in 2004 that had a
financial impact of $160 million (Koch, 2004a) and Nike’s failure in 2000 that cost $100 million
in sales and resulted in a 20% drop in stock price (Koch, 2004b). Low adoption and
underutilization of ITs have been suggested to be key reasons for “productivity paradox”—that
is, a contradictory relationship between IT investment and firm performance (Devaraj & Kohli,
2003; Landauer, 1995; Sichel, 1997). This issue is particularly important given that recent
reports suggest that worldwide investment in IT will increase at a rate of 7.7% a year from 2004
to 2008 compared to 5.1% from 2000 to 2004 (WITSA, 2004). It has been suggested in both the
academic and trade press that managers need to develop and implement effective interventions in
order to maximize employees’ IT adoption and use (e.g., Cohen, 2005; Jasperson, Carter, &
Zmud, 2005). Therefore, identifying interventions that could influence employee adoption and
use of new ITs can aid managerial decision making on successful IT implementation strategies
(Jasperson et al., 2005).
The theme of interventions as an important direction for future research is documented in
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recent research. For instance, Venkatesh (2006) reviewed prior research on IT adoption and
suggested three avenues for future research that are pertinent to the editorial mission of Decision
Sciences: (i) business process change and process standards; (ii) supply-chain technologies; and
(iii) services. Within each of these three avenues, he noted interventions as a critical direction for
future research that had significant managerial implications and the potential to enhance IT
implementation success. More recently, other researchers have provided new directions in
individual-level IT adoption research with a particular focus on interventions that can potentially
lead to greater acceptance and effective utilization of IT (e.g., Benbasat & Barki, 2007;
Goodhue, 2007; Venkatesh, Davis, & Morris, 2007). Our objective is to present a brief literature
review, propose an integrated model of employee decision-making about new ITs, empirically
validate the model, and present a research agenda that identifies a set of interventions for
researchers and practitioners to investigate to further our understanding of IT implementation.
The research on individual-level IT adoption and use is mature and has provided rich
theories and explanations of the determinants of adoption and use decisions (e.g., Venkatesh et
al. 2003; Sarker, Valacich, & Sarker, 2005 for group-level IT adoption research).
Notwithstanding the plethora of IT adoption studies, there has been limited research on the
interventions that can potentially lead to greater acceptance and use of IT (e.g., Venkatesh,
1999). The most widely-employed model of IT adoption and use is the technology acceptance
model that has been shown to be highly predictive of IT adoption and use (Adams, Nelson, &
Todd, 1992; Davis, Bagozzi, & Warshaw, 1989; Venkatesh & Morris, 2000; Venkatesh & Davis,
2000). One of the most common criticisms of TAM has been the lack of actionable guidance to
practitioners (Lee, Kozar, & Larsen, 2003). Many leading researchers have noted this limitation
in interviews reported in Lee et al. (2003). For example, Alan Dennis commented, “imagine
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talking to a manager and saying that to be adopted technology must be useful and easy to use. I
imagine the reaction would be ‘Duh! The more important questions are what [sic] makes
technology useful and easy to use” (Lee et al., 2003, p. 766). Some work has been done to
address this limitation by identifying determinants of key predictors in TAM, namely, perceived
usefulness and perceived ease of use. Some researchers have developed context-specific
determinants to the two TAM constructsfor instance, Karahanna and Straub (1999) for
electronic communication systems (i.e., e-mail systems), Koufaris (2002) for e-commerce, Hong
and Tam (2006) for multipurpose information appliances, Rai and Patnayakuni (1996) for CASE
tools, and Rai and Bajwa (1997) for executive information systemsthat have immense value in
theorizing richly about the specific IT artifact (type of system) in question and identifying
determinants that are specific to the type of technology being studied. Others have developed
general and context-independent determinants that span across a broad range of systems (e.g.,
Venkatesh, 2000; Venkatesh & Davis, 2000). While each of these approaches has merits and it is
not our goal to debate generality vs. context specificity in theorizing (Bacharach, 1989; Johns,
2006), in this paper, we are choosing the general set of determinants of TAM as a basis for the
identification of broadly-applicable interventions that can fuel a future research agenda.
Venkatesh and Davis (2000) identified general determinants of perceived usefulness and
Venkatesh (2000) identified general determinants of perceived ease of use. These two models
were developed separately and not much is known about possible crossover effectsthat is,
could determinants of perceived usefulness influence perceived ease of use and/or could
determinants of perceived ease of use influence perceived usefulness? Investigating and
theorizing about potential crossover effects or ruling out the possibility of these effects is an
important step in developing a more comprehensive nomological network around TAM. Further,
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interventions, based on the determinants of perceived usefulness and perceived ease of use, hold
the key to helping managers make effective decisions about applying specific interventions to
influence the known determinants of IT adoption and, consequently, the success of new ITs
(DeLone & McLean, 2003; Rai, Lang, & Welker, 2002; Sabherwal, Jeyaraj, & Chowa, 2006).
Given this backdrop, this paper presents an integrated model of determinants of perceived
usefulness and perceived ease of use, empirically validates the model, and uses the integrated
model as a springboard to propose future directions for research on interventions.
Background
TAM was developed to predict individual adoption and use of new ITs. It posits that individuals’
behavioral intention to use an IT is determined by two beliefs: perceived usefulness, defined as
the extent to which a person believes that using an IT will enhance his or her job performance,
and perceived ease of use, defined as the degree to which a person believes that using an IT will
be free of effort. It further theorizes that the effect of external variables (e.g., design
characteristics) on behavioral intention will be mediated by perceived usefulness and perceived
ease of use. Over the last two decades, there has been substantial empirical support in favor of
TAM (e.g., Adams et al., 1992; Agarwal & Karahanna, 2000; Karahanna, Agarwal, & Angst,
2006; Venkatesh et al., 2003, 2007). TAM consistently explains about 40% of the variance in
individuals’ intention to use an IT and actual usage. As of December 2007, the Social Science
Citation Index listed over 1,700 citations and Google Scholars listed over 5,000 citations to the
two journal articles that introduced TAM (Davis, 1989; Davis et al., 1989).
Theoretical Framework
Prior research employing TAM has focused on three broad areas. First, some studies
replicated TAM and focused on the psychometric aspects of TAM constructs (e.g., Adams et al.,
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1992; Hendrickson, Massey, & Cronan, 1993; Segars & Grover, 1993). Second, other studies
provided theoretical underpinning of the relative importance of TAM constructsthat is,
perceived usefulness and perceived ease of use (e.g., Karahanna, Straub, & Chervany, 1999).
Finally, some studies extended TAM by adding additional constructs as determinants of TAM
constructs (e.g., Karahanna & Straub, 1999; Koufaris, 2002; Venkatesh, 2000; Venkatesh &
Davis, 2000). Synthesizing prior research on TAM, we developed a theoretical framework that
represents the cumulative body of knowledge accumulated over the years from TAM research
(see Figure 1). The figure shows four different types of determinants of perceived usefulness and
perceived ease of useindividual differences, system characteristics, social influence, and
facilitating conditions. Individual difference variables include personality and or demographics
(e.g., traits or states of individuals, gender, and age) that can influence individuals’ perceptions
of perceived usefulness and perceived ease of use. System characteristics are those salient
features of a system that can help individuals develop favorable (or unfavorable) perceptions
regarding the usefulness or ease of use of a system. Social influence captures various social
processes and mechanisms that guide individuals to formulate perceptions of various aspects of
an IT. Finally, facilitating conditions represent organizational support that facilitates the use of
an IT.
---------------------------------------
Insert Figure 1 about here
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Determinants of Perceived Usefulness
Venkatesh and Davis (2000) proposed an extension of TAMTAM2by identifying and
theorizing about the general determinants of perceived usefulnessthat is, subjective norm,
image, job relevance, output quality, result demonstrability, and perceived ease of useand two
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moderatorsthat is, experience and voluntariness. The first two determinants fall into the
category of social influence and the remaining determinants are system characteristics as per the
theoretical framework shown in Figure 1. Table 1 provides the definitions of the determinants of
perceived usefulness. TAM2 presents two theoretical processessocial influence and cognitive
instrumental processesto explain the effects of the various determinants on perceived
usefulness and behavioral intention. In TAM2, subjective norm and image are the two
determinants of perceived usefulness that represent the social influence processes. Drawing on
Kelman’s (1958, 1961) work on social influence and French and Raven’s (1959) work on power
influences, TAM2 theorizes that three social influence mechanismscompliance,
internalization, and identificationwill play a role in understanding the social influence
processes. Compliance represents a situation in which an individual performs a behavior in order
to attain certain rewards or avoid punishment (Miniard & Cohen, 1979). Identification refers to
an individual’s belief that performing a behavior will elevate his or her social status within a
referent group because important referents believe the behavior should be performed (Venkatesh
& Davis, 2000). Internalization is defined as the incorporation of a referent’s belief into one’s
own belief structure (Warshaw, 1980). TAM2 posits that subjective norm and image will
positively influence perceived usefulness through processes of internalization and identification
respectively. It further theorizes that the effect of subjective norm on both perceived usefulness
and behavioral intention will attenuate over time as users gain more experience with a system.
---------------------------------------
Insert Table 1 about here
---------------------------------------
In TAM2, four constructsjob relevance, output quality, result demonstrability, and
perceived ease of usecapture the influence of cognitive influence processes on perceived
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usefulness. Drawing on three different theoretical paradigmsthat is, work motivation theory
(e.g., Vroom, 1964), action identification theory (e.g., Vallacher & Wegner, 1987), and
behavioral decision theory (e.g., Beach & Mitchell, 1996, 1998), Venkatesh and Davis (2000)
provided a detailed discussion of how and why individuals form perceptions of usefulness based
on cognitive influence processes. The core theoretical argument underlying the role of cognitive
instrumental processes is that individuals “form perceived usefulness judgment in part by
cognitively comparing what a system is capable of doing with what they need to get done in their
job” (Venkatesh & Davis, 2000, p. 190). TAM2 theorizes that individuals’ mental assessment of
the match between important work goals and the consequences of performing job tasks using a
system serves as a basis for forming perceptions regarding the usefulness of the system
(Venkatesh & Davis, 2000). TAM2 posits that perceived ease of use and result demonstrability
will have a positive direct influence on perceived usefulness. Job relevance and output quality
will have a moderating effect on perceived usefulness such that the higher the output quality the
stronger the effect job relevance will have on perceived usefulness. Venkatesh and Davis found
strong support for TAM2 in longitudinal field studies conducted at four organizations.
Determinants of Perceived Ease of Use
Building on the anchoring and adjustment framing of human decision making, Venkatesh (2000)
developed a model of the determinants of perceived ease of use. Venkatesh (2000) argued that
individuals will form early perceptions of perceived ease of use of a system based on several
anchors related to individuals’ general beliefs regarding computers and computer use. The
anchors suggested by Venkatesh (2000) are computer self-efficacy, computer anxiety, and
computer playfulness, and perceptions of external control (or facilitating conditions). The first
three of these anchors represent individual differences as per Figure 1that is, general beliefs
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associated with computers and computer use. Computer self-efficacy refers to individuals’
control beliefs regarding his or her personal ability to use a system. Perceptions of external
control are related to individuals’ control beliefs regarding the availability of organizational
resources and support structure to facilitate the use of a system. Computer playfulness represents
the intrinsic motivation associated with using any new system. Venkatesh (2000) suggested that
while anchors drive initial judgments of perceived ease of use, individuals will adjust these
judgments after they gain direct hands-on experience with the new system. Two system
characteristics-related adjustmentsthat is, perceived enjoyment and objective usabilitywere
suggested by Venkatesh (2000) to play a role in determining perceived ease of use after
individuals gain experience with the new system. Venkatesh (2000) theorized that even with
increasing experience with the system, the role of two anchorscomputer self efficacy and
perceptions of external controlwill continue to be strong. However, the effects of the other two
anchorscomputer playfulness and computer anxietywere theorized to diminish over time.
Venkatesh (2000) further theorized that the effects of adjustments on perceived ease of use were
stronger with more hands-on experience with the system. Although longitudinal studies were
conducted, the specific moderating role by experience was not tested in Venkatesh (2000).
---------------------------------------
Insert Table 2 about here
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Development of TAM3
We combine TAM2 (Venkatesh & Davis, 2000) and the model of the determinants of perceived
ease of use (Venkatesh, 2000), and develop an integrated model of technology acceptance
TAM3, shown in Figure 2. TAM3 presents a complete nomological network of the determinants
of individuals’ IT adoption and use. We suggest three theoretical extensions beyond TAM2 and
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the model of the determinants of perceived ease of use. In this section, we discuss these
theoretical extensions and the rationale for the integration.
---------------------------------------
Insert Figure 2 about here
---------------------------------------
Crossover Effects
We expect the general pattern of relationships suggested in Venkatesh and Davis (2000) and
Venkatesh (2000) to hold in TAM3. Further, we suggest that the determinants of perceived
usefulness will not influence perceived ease of use and the determinants of perceived ease of use
will not influence perceived usefulness. Thus, TAM3 does not posit any cross-over effects.
As noted earlier, two theoretical processes explain the relationships between perceived
usefulness and its determinants: social influence and cognitive instrumental processes. The
effects of the various factorsthat is, subjective norm, image, job relevance, output quality, and
result demonstrabilityon perceived usefulness are tied to these two processes. We have no
theoretical and empirical basis to expect that these processes will play any role in forming
judgments about perceived ease of use. Perceived ease of use has been theorized to be closely
associated with individuals’ self-efficacy beliefs and procedural knowledge, which requires
hands-on experience and execution of skills (Davis et al., 1989; Davis & Venkatesh, 2004;
Venkatesh, 2000). Further, Venkatesh (2000) suggested that individuals form perceived ease of
use about a specific system by anchoring their perceptions to the different general computer
beliefs and later adjusting their perceptions of ease of use based on hands-on experience with the
specific system. Social influence processes (i.e., compliance, identification, and internalization)
in the context of IT adoption and use represent how important referents belief about the
instrumental benefits of using a system (Venkatesh & Davis, 2000). Even if an individual gets
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information from important referents about how easy a system is to use, it is unlikely that the
individual will form stable perceptions of ease of use based on the beliefs of referent others over
and above his or her own general computer beliefs and hands-on experience with the system
(e.g., Davis & Venkatesh, 2004). Further, the determinants of perceived ease of use represent
several traits and emotions, such as computer self-efficacy, computer playfulness, and computer
anxiety. There are no theoretical and empirical reasons to believe that these stable computer-
related traits and emotions will be affected by social influence or cognitive influence processes.
We suggest that the determinants of perceived ease of use will not influence perceived
usefulness. The determinants of perceived ease of use suggested by Venkatesh (2000) are
primarily individual differences variables and general beliefs about computers and computer use.
These variables are grouped into three categories: control beliefs, intrinsic motivation, and
emotion. Perceived usefulness is an instrumental belief that is conceptually similar to extrinsic
motivation and is a cognition (as opposed to emotion) regarding the benefits of using a system.
The perceptions of control (over a system), enjoyment or playfulness related to a system, and
anxiety regarding the ability to use a system do not provide a basis for forming perceptions of
instrumental benefits of using a system. For example, control over using a system does not
guarantee that the system will enhance one’s job performance. Similarly, higher levels of
computer playfulness or enjoyment from using a system do not mean that the system will help an
individual to become more effective (e.g., Van der Heijden, 2004). Therefore, we expect that the
determinants of perceived ease of use will not influence perceived usefulness.
New Relationships Posited in TAM3
TAM3 posits three relationships that were not empirically tested in Venkatesh (2000) and
Venkatesh and Davis (2000). We suggest that experience will moderate the relationships
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between (i) perceived ease of use and perceived usefulness; (ii) computer anxiety and perceived
ease of use; and (iii) perceived ease of use and behavioral intention.
Perceived ease of use to perceived usefulness, moderated by experience
We suggest that with increasing hands-on experience with a system, a user will have more
information on how easy or difficult the system is to use. While perceived ease of use may not be
as important in forming behavioral intention in a later period of system use (Venkatesh et al.,
2003), users will still value perceived ease of use in forming perceptions about usefulness. We
base this argument on action identification theory (Vallacher & Kaufman, 1996) that posits a
clear distinction between high-level and low-level action identities. High-level identities are
related to individuals’ goals and plans, whereas low-level identities refer to the means to achieve
these goals and plans. For instance, in the context of a word processing software use, a high-level
identity can be writing a high quality report and a low-level identity can be striking keys or use
of a specific feature of the software (Davis & Venkatesh, 2004). Perceived usefulness and
perceived ease of use are considered high-level and low-level identities respectively (Davis &
Venkatesh, 2004; Venkatesh & Davis, 2000). We suggest that, with increasing experience, the
influence of perceived ease of use (a low-level identity) on perceived usefulness (a high-level
identity) will be stronger as users will be able to form an assessment of their likelihood of
attaining high-level goals (i.e., perceived usefulness) based on information gained from
experience of the low-level actions (i.e., perceived ease of use).
Computer anxiety to perceived ease of use, moderated by experience
Experience will moderate the effect of computer anxiety on perceived ease of use, such that with
increasing experience, the effect of computer anxiety on perceived ease of use will diminish. We
expect that, with increasing experience, system-specific beliefs, rather than general computer
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beliefs, will be stronger determinants of perceived ease of use of a system. Venkatesh (2000)
argued that system-specific objective usability and perceived enjoyment will be stronger
determinants over time and the effects of general computer beliefs (e.g., computer anxiety) will
diminish because with increasing experience, users will develop accurate perceptions of effort
required to complete specific tasks (i.e., objective usability) and discover aspects of a system that
lead to enjoyment (or lack thereof). Computer anxiety is theorized as an anchoring belief that
inhibits forming a positive perception of ease of use of a system (Venkatesh, 2000). Research on
anchoring and adjustment has found that while anchors influence judgments, the role of anchors
declines over time as adjustment information becomes available (Mussweiler & Strack, 2001;
Wansink, Kent, & Hoch, 1998; Yadav, 1994). Drawing on this, we argue that the effect of
computer anxiety on perceived ease of use will decline with increasing experience as individuals
will have more accurate perceptions of the effort needed to use a system.
Perceived ease of use to behavioral intention, moderated by experience
We expect that experience will moderate the effect of perceived ease of use on behavioral
intention such that the effect will be weaker with increasing experience. Perceived ease of use
that is, how easy or difficult a system is to useis an initial hurdle for individuals while using a
system (Venkatesh, 2000). However, once individuals get accustomed to the system and gain
hands-on experience with the system, the effect of perceived ease of on behavioral intention will
recede into the background as individuals now have more procedural knowledge about how to
use the system. Consequently, individuals will place less importance on perceived ease of use
while forming their behavior intentions to use the system.
METHOD
Longitudinal field studies were conducted to test TAM3. Data were collected from four different
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organizationssites A through Dimplementing new ITs. These organizations provided an
opportunity to test our research model in real-world settings of IT implementations. The research
sites represented different industries, organizational contexts, and functional areas. Further, the
types of ITs were different across the sites. Such variability in organizational settings and types
of technologies adds to the potential generalizability of our findings. In two of these
organizations, the use of the new system was voluntary. In all four organizations, we collected
data over a five-month period with four points of measurements. In this section, we describe the
settings, participants, measurement, and data collection procedure.
Settings and Participants
Site A was a medium-sized manufacturing firm that introduced a proprietary operational system
to manage daily operations such as floor and machine scheduling and personnel assignment.
These operations were conducted manually by the floor supervisors before the implementation of
the new system. The users received two days of formal training on the new system. The users of
the new system were 48 floor supervisors of whom 38 completed the survey at all points of
measurement. The use of the new system was voluntary.
Site B was a large financial services firm that was in the process of transitioning to a
Windows-based environment from mainframe-based IT applications. The users were members of
the personal financial services department. The system use was voluntary as the users were
allowed to use the old systems. Formal on-site training about the system was conducted for one-
and-a-half days. Out of 50 potential users of the system who participated in the training, 39
provided usable responses at all points of measurement.
Site C was a small accounting services firm that introduced a new Windows-based
customer account management system replacing the old paper- and DOS-based systems. The
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users were from customer service representatives. The system use was mandatory as the old
system was phased out immediately after the new system implementation. On-site system
training was conducted for one day. Out of 51 potential users of the new system who attended
the training, 43 provided usable responses at all points of measurement.
Site D was a small international investment banking firm that implemented a new system
to assist in analyzing and creating financially sound international stock portfolios. The users
were analysts performing different functions related to domestic and international stock
management. While the organization had an existing system to perform the activities related to
analyzing and creating stock portfolios, the new system had substantially different features and
was developed by a different vendor. The use of the system was mandatory. The potential users
received a four-hour long training program to become familiar with the new system. Out of 51
potential users of the new system, 36 provided usable response at all points of measurement.
Measurement
We used validated items from prior research to test TAM3. Appendix A presents a list of items
for all the constructs. TAM constructsthat is, perceived usefulness (PU), perceived ease of use
(PEOU), and behavioral intention (BI)were operationalized using items adapted from Davis
(1989) and Davis et al. (1989). Consistent with Davis (1989), use (USE) was operationalized by
asking the respondents, “On average, how much time to you spend on the system every day? ___
hours and ___ minutes.” Our research design allowed us to collect the use data separate from its
determinants (e.g., behavioral intention, perceived usefulness, etc.). Particularly, there was at
least one month gap between the collection of survey data and the measurements of use. For
example, the measurements of use and its determinants were separated by one month (T1-T2),
three months (T2-T3) and two months (T3-T4). Such a design approach helped us overcome the
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problems associated with common method biases.
Operationalization of the determinants of perceived ease of use (i.e., computer self-
efficacy, perceptions of external control, computer playfulness, computer anxiety, objective
usability, and perceived enjoyment) was consistent with Venkatesh (2000). Computer self-
efficacy (CSE) was measured using four items adapted from Compeau and Higgins (1995a).
Perceptions of external control (PEC) were measured using four items adapted from the scale of
facilitating conditions developed by Mathieson (1991) and Taylor and Todd (1995). Computer
playfulness (CPLAY) was measured using four items adapted from Webster and Martocchio
(1992). Computer anxiety (CANX) was measured using four items used in Venkatesh (2000).
Following Venkatesh (2000) and human-computer interaction (HCI) research, objective usability
(OU) was operationalized by computing a novice-to-expert ratio of effort. During the training
program, each participant was asked to perform a set of tasks using the new system. The system
recorded the time each participant took to accomplish the tasks. The time was then compared to
the time taken by an expert to accomplish the same tasks to determine a ratio which served as the
measure of objective usability for each participant. Perceived enjoyment (ENJ) was measured
using four items adapted from Davis, Bagozzi, and Warshaw (1992).
Determinants of perceived usefulness were measured using items from Venkatesh and
Davis (2000). Subjective norm (SN) was measured using four items adapted from Taylor and
Todd (1995). Image (IMG) and result demonstrability (RES) were operationalized using three
and four items respectively from Moore and Benbasat (1991). Job relevance (REL) and output
quality (OUT) were measured using three items each adapted from Davis et al. (1992).
Voluntariness (VOL) was assessed using three items from Moore and Benbasat (1991). Even
though we chose two sites where system use was voluntary and two sites where the use was
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mandatory, we collected data on user perceptions of voluntariness because, consistent with
TAM2, TAM3 posits perceived, rather than actual, voluntariness as an important contextual
variable.
Procedure
As noted earlier, formal training was conducted at each site to educate the potential users about
the new system. While the duration and method of this formal training varied in different sites,
our data collection approach was consistent across the four sites. In all four organizations, we
administered questionnaires at three points in time: after initial training (T1), one month after
implementation (T2), and three months after implementation (T3). We also measured self-
reported usage at T2, T3, and five months after implementation (T4). We administered the T1
survey immediately after the formal training at each site. We captured each participant’s login ID
and assigned a unique barcode for each participant. This unique barcode helped us track
individual responses in subsequent data collection period (T2, T3, and T4). Self-reported use
related to the previous period was measured (e.g., at T2, use from T1 to T2 was measured). The
T2 and T3 surveys were paper-based. The paper-based surveys with the unique barcodes were
delivered to the mailboxes of each participant who filled out surveys at T1 with a request to
return the surveys within a week to the researchers. At T4, only self-reported use was measured.
RESULTS
We used Partial Least Squares (PLS), a component-based structural equation modeling
technique, to analyze our data. PLS-Graph, version 3, build 1126 was used to analyze the data.
Chin, Marcolin, and Newsted (2003) noted that PLS has minimal restrictions in terms of
distributional assumptions and sample size. While analyzing data, we followed the guidelines
specified in Chin et al. (2003) and other exemplars in IS research (e.g., Compeau & Higgins,
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1995a). All constructs were modeled using reflective indicators. Consistent with Venkatesh and
Davis (2000) and Venkatesh et al. (2003), voluntariness was coded per the score for each
participant and experience was coded as an ordinal variable. When applicable, we mean-centered
the variables at the indicator level prior to creating the interaction terms (Aiken & West, 1991;
Chin et al., 2003). Mean-centering helps limit potential multicollinearity, evidenced by the low
variation inflation factors (VIFs) for all constructs in our model. We employed a bootstrapping
method (500 times) that used randomly selected subsamples to test the various PLS models.
Measurement Models
We assessed the measurement model separately for each time period (N=156 for each time
period). All constructs at each time period exhibited strong psychometric properties and satisfied
the criteria of reliability and convergent and discriminant validity. Table 3 shows that the item
loadings were greater than or at least equal to .70 for all constructs at all time periods. We did
not find any cross-loadings of more than .30. Thus, convergent and discriminant validity was
supported (Fornell & Larcker, 1981). As Table 4 shows, internal consistency reliabilities (ICRs)
were greater than .70 for all constructs at all points of measurement. The square root of the
average variance extracted (AVE) for each construct was higher than the correlations across
constructs. Such strong psychometric properties were consistent with much prior research
employing these constructs and measures (e.g., Agarwal & Karahanna, 2000; Davis, 1989; Davis
et al., 1989; Karahanna et al., 2006; Mathieson, 1991; Taylor & Todd, 1995). The pattern of
correlations shown in Table 4 is consistent with prior studies (e.g., Venkatesh et al., 2003).
While the longitudinal design provided us a procedural remedy for common method bias, we
conducted statistical analysis following the guidelines of Podsakoff, MacKenzie, Lee, and
Podsakoff (2003) and Malhotra, Kim, and Patil (2006) to assess common method bias.
19
Particularly, we conducted Harmon’s single factor test and marker variable test (we used job
satisfaction as a marker variable) and did not find any significant common method bias.
---------------------------------------
Insert Tables 3 and 4 Here
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Explaining and Predicting Perceived Usefulness
Our findings regarding perceived usefulness were generally consistent with Venkatesh and Davis
(2000). In particular, we found that perceived ease of use, subjective norm, image, and result
demonstrability were significant predictors of perceived usefulness at all time periods (see Table
5). Also consistent with Venkatesh and Davis (2000), we found that job relevance and output
quality had an interactive effect on perceived usefulness such that with increasing output quality,
the effect of job relevance on perceived usefulness was stronger. We found that experience
moderated the effects of subjective norm on perceived usefulness such that the effect was weaker
with increasing experience. While not shown in Table 5, we found that the effect of image on
subjective norm was significant at all points of measurements.
TAM3 posits that: (1) the effect of perceived ease of use on perceived usefulness will be
moderated by experience; and (2) the determinants of perceived ease of use (i.e., computer self-
efficacy, perceptions of external control, computer anxiety, computer playfulness, perceived
enjoyment, and objective usability) will not have any significant effects on perceived usefulness
over and above the determinants of perceived usefulness. As shown in Table 5, experience
moderated the effect of perceived ease of use on perceived usefulness such that with increasing
experience the effect became stronger. The table also shows that none of the determinants of
perceived ease of use had significant effects on perceived usefulness at any point in time.
Overall, TAM3 was able to explain between 53% and 67% of the variance in perceived
20
usefulness across different time periods and models (see Table 5).
---------------------------------------
Insert Table 5 Here
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Explaining and Predicting Perceived Ease of Use
Consistent with Venkatesh (2000), we found that the anchorsthat is, computer self-efficacy,
perceptions of external control, computer anxiety, and computer playfulnesswere significant
predictors of perceived ease of use at all points of measurement (see Table 6). As expected, the
adjustmentsthat is, perceived enjoyment and objective usabilitywere not significant at T1,
but they were significant at both T2 and T3. As theorized, we found that experience moderated
the effect of computer anxiety on perceived ease of use such that the effect became weaker with
increasing experience (CANX X EXP). Our results indicated that none of the determinants of
perceived usefulness had a significant effect on perceived ease of use. Overall, TAM3 explained
between 43% and 52% of variance in perceived ease of use across different points of
measurements and models (see Table 6).
---------------------------------------
Insert Table 6 Here
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Explaining and Predicting Behavioral Intention and Use
We found that perceived usefulness was the strongest predictor of behavioral intention at all time
periods (see Table 7). While perceived ease of use was significant at T1 and T2, it was not
significant at T3, suggesting a moderating effect of experience in the relationship between
perceived ease of use and behavioral intention. We found that experience in fact moderated the
effect of perceived ease of use (PEOU X EXP) on behavioral intention such that with increasing
21
experience the effect became weaker. We also found a significant three-way interaction among
subjective norm, experience, and voluntariness (SN X EXP X VOL) on behavioral intention such
that the effect of subjective norm on behavioral intention became weaker with increasing
experience, particularly in the voluntary context. The two-way interaction between subjective
norm and voluntariness (SN X EXP) indicated that the effect of subjective norm on behavioral
intention was stronger in a mandatory context. Table 7 shows that TAM3 explained between
40% and 53% variance in behavioral intention across time periods and models. Consistent with
much prior research on IT adoption and social psychology, we found that behavioral intention
was a significant predictor of use at all points of measurements. Table 8 shows that the variance
explained in use was between 31% and 36%.
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Insert Tables 7 and 8 Here
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INTERVENTIONS AND FUTURE RESEARCH DIRECTIONS
The development and validation of TAM3 was an important first step in understanding the role
of interventions in IT adoption contexts. In this section, we discuss important interventions based
on the determinants of perceived usefulness and perceived ease of use and offer future research
directions on these interventions. We classify the potential interventions into two categories: pre-
implementation and post-implementation interventions. Our classification approach was
motivated by the stage models of IT implementation suggested by Cooper and Zmud (1990) and
Saga and Zmud (1994). These stage models identified important activities and user reactions
during pre- and post-implementation phases of IT implementation. The pre-implementation
phase is characterized by stages leading to the actual roll-out of a systemi.e., initiation,
organizational adoption, and adaptationwhile the post-implementation phase entails stages that
22
follows the actual deployment of the systemthat is, user acceptance, routinization, and infusion
(Cooper & Zmud, 1990). Initiation: Identification of organizational problems/opportunities that
warrant a technology solution; adoption: organizational decision to adopt and install a
technology; adaptation: modification processes directed toward individual/organizational needs
to better fit the technology with the work setting; acceptance: efforts undertaken to induce
organizational members to commit to the use of technology; routinization: alterations that occur
within work systems to account for technology such that these systems are no longer perceived
as new or out-of-the ordinary; infusion: technology becomes more deeply embedded within the
organization’s work system (Cooper & Zmud, 1990; Saga & Zmud, 1994). Table 9 presents a
summary of pre- and post-implementation interventions and their potential influence on the
determinants of perceived usefulness and perceived ease of use. We use this table as a
framework in the subsequent discussion.
---------------------------------------
Insert Table 9 Here
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Pre-implementation Interventions
Pre-implementation interventions represent a set of organizational activities that take place
during system development and deployment periods and can potentially lead to greater
acceptance of a system. These interventions are important for at least two interrelated reasons: (i)
minimization of initial resistance to a new system; and (ii) providing a realistic preview of the
system so that potential users can develop an accurate perception regarding system features and
how the system may help them perform their job. As systems are becoming increasingly
complex and central to managerial and employee decision making and work processes (e.g.,
enterprise resource planning, supply chain management, customer relationships management
23
systems) requiring substantial changes to organizational business processes, implementation of
such complex, disruptive systems are subject to severe resistance from employees (see
Venkatesh, 2006). Employees may feel that the new system will threaten their existing routines
and habits, change the nature of their job and relationships with others, and degrade their status
in the organizations (Beaudry & Pinnsonnealt, 2005; Lapointe & Rivard, 2005; Markus, 1983).
Proactive implementation of interventions is thus necessary to minimize such resistance.
Furthermore, employees may perceive that the complexity of a new system will add quantitative
and qualitative overload to their job and reduce autonomy and control over their work
environment (Ahuja & Thatcher, 2005). This perception may result from an inaccurate
understanding of system characteristics and instrumental benefits of the system (Davis &
Venkatesh, 2004). Therefore, interventions that ensure accurate perceptions of system
characteristics and instrumental benefits of a system are of immense importance during per-
implementation phase.
Design characteristics
Design characteristics of a system can positively influence user acceptance and system success
(e.g., Davis, 1993; DeLone & McLean, 1992, 2003; Wixom & Todd, 2005). These
characteristics can be broadly categorized into information- and system-related characteristics
(DeLone & McLean, 1992). We suggest that information-related characteristics of a system will
influence the determinants of perceived usefulness, while the system-related characteristics will
influence the determinants of perceived ease of use. For example, in the context of group support
systems, prior research has suggested the information-related design characteristics help users
improve productivity and performance (e.g., Dennis & Valacich, 1993, 1999; Dennis, Valacich,
Carte, Garfield, Haley, & Aronson, 1997; Speier, Valacich, & Vessey, 1999; Valacich, Dennis,
24
& Connolly, 1994). If a system can provide users relevant information in a timely manner,
accurately, and in understandable format and help them make better decisions (e.g., Speier,
Valacich, & Vessey, 2003), it is more likely that users will perceive greater job relevance of the
system, high output quality, and greater result demonstrabilitythe important determinants of
perceived usefulness. Related yet distinct from this, if a system is reliable (e.g., no downtime),
flexible, and user friendlyimportant aspects of system-related characteristicsit is more likely
that the users will perceive their use experience to be enjoyable and have less system-related
anxiety. The system-related characteristics will enhance objective usability of the system because
users will be able to perform their tasks quickly. Further, it is possible that if the system is user
friendly, a user may feel that they have a greater control over the system, thus enhancing their
self-efficacy toward using the system. Design characteristics are particularly important for
complex systems because these systems are inherently difficult to understand and use.
We urge IS researchers to examine the influence of design characteristics on user
acceptance, particularly on the determinants of perceived usefulness and perceived ease of use.
While prior research (e.g., Wixom & Todd, 2005) found that information and system quality
influenced perceived usefulness and perceived ease of use, we suggest that it is important to drill
down into what design characteristics influence what specific aspects of perceived usefulness
and perceived ease of use in order to enhance our ability to identify and improve specific design
characteristics to enhance certain determinants of perceived usefulness and perceived ease of
use. From a methodological point of view, we understand that manipulating design
characteristics in a field setting can be difficult and expensive. Simulation and agent-based
modeling approaches (e.g., Macy & Willer, 2002; Raghu, Rao, & Sen, 2003) offer low cost
alternatives to investigate the impact of design characteristics on IT adoption and use. These
25
approaches can be used to manipulate different design characteristics and isolate the effects of
these characteristics on various determinants of IT adoption. Example research questions related
to design characteristics are:
i. What specific design characteristics will influence the determinants of perceived usefulness
and perceived ease of use?
ii. How can users be helped so that they develop accurate perceptions of design characteristics
during the implementation phases of IT implementation, particularly for complex systems that
are traditionally perceived as difficult to understand and use?
iii. Will perceived usefulness and perceived ease of use formed based on early preview of design
characteristics of complex systems remain stable throughout the implementations process?
User participation
User participation refers to the assignments, activities, and behaviors that users or their
representatives perform during the systems implementation process (Barki & Hartwick, 1994). It
is an important intervention that has been shown to lead to greater user involvement, system
acceptance, and system success (Hartwick & Barki, 1994; Ives & Olson, 1984; Swanson, 1974).
We suggest that user participation is even more important for complex, enterprise systems as
these systems are expected to cause substantial disruptions of organizational work processes.
Even though user participation and involvement have been used interchangeably in the IS
literature, Barki and Hartwick (1994) and Hartwick and Barki (1994) provided conceptual
distinctions between the two. They argue that user participation refers to the actual partaking in a
project, whereas user involvement refers to a subjective psychological state reflecting the
importance and personal relevance of a new system to the user. The three dimensions of user
participationthat is, overall responsibility (e.g., leadership and accountability in the system
26
implementation process), user-IS relationship (e.g., user-IS communication and influence), and
hands-on activity (e.g., specific tasks related to system implementation performed by the
users)will help users develop accurate perceptions of system characteristics and the benefits of
the system (Barki & Hartwick, 1994; Hartwick & Barki, 1994). We suggest that if users or their
representatives participate in the system development and implementation activities (e.g., system
evaluation and customization, prototype testing, business process change initiatives), it is more
likely that they will be able to form judgments about job relevance, output quality, and result
demonstrabilitythe important determinants of perceived usefulness. Participation and
involvement will lead to a greater understanding of top management’s view toward the system
and thus, form opinions regarding the social pressurethat is, subjective norm. We further
suggest that participation through hands-on activity may reduce anxiety related to system use and
can potentially enhance favorable perceptions of external control, perceived enjoyment, and
objective usability because the users will have a better understanding of the system features,
organizational resources, and supports pertinent to the system.
While prior research has suggested the importance of user participation and involvement
in predicting system success, there is a need to understand whether, how, and why user
participation and involvement influence the determinants of perceived usefulness and perceived
ease of use, particularly in the context of complex systems. Such an understanding will help
managers make decisions about effective change management strategies. Some illustrative
research questions are:
i. For what type of system is user participation an effective pre-implementation intervention?
ii. Should all potential users be involved in a project or can a subset of users be involved? What
is the optimal number of users who should be involved?
27
iii. What are the effects of the different ways of user participation (e.g., joint application
development, membership in project team, preview of system and business process
characteristics) on the key determinants of perceived usefulness and perceived ease of use and
consequently, perceived usefulness and perceived ease of use?
Management support
Management support refers to the degree to which an individual believes that management has
committed to the successful implementation and use of a system. While management support has
been suggested as an important antecedent of IT implementation success (e.g., Jarvenpaa & Ives,
1991; Leonard-Barton & Deschamps, 1988; Liang, Saraf, Hu, & Xue, 2007; Markus, 1981;
Sharma & Yetton, 2003), it was not conceptualized as an intervention that can influence the
determinants of user acceptance. Jasperson et al. (2005) suggested that managers (e.g., direct
supervisors, middle managers, and senior executives) are important sources of interventions.
Management can intervene indirectly (e.g., sponsoring or championing, providing resource, and
issuing directives and/or mandates) or directly (e.g., using features of IT, directing modification
or enhancement of IT applications, incentive structures, or work tasks/processes) in the
implementation process of an IT (Jasperson et al., 2005). Prior research has suggested one of the
most critical success factors for complex systems (e.g., enterprise systems) is management
support and championship (Chatterjee, Grewal, & Sambamurthy, 2002; Holland & Light, 1999;
Liang et al., 2007; Purvis, Sambamurthy, & Zmud, 2001). Because the implementation of these
systems often requires substantial changes to organizational structure, employees’ role and job,
reward systems, control and coordination mechanisms, and work processes, top management’s
supports in the form of commitment and communication related to system implementation are
absolutely critical for the legitimacy of the implementation process and employee morale
28
following the implementation. We suggest management support can influence users’ perceptions
of subjective norm and imagetwo important determinants of perceived usefulness. We further
suggest that management support, particularly in the form of direct involvement in the system
development and implementation processes (Jasperson et al., 2005), will help employees form
judgments regarding job relevance, output quality, and result demonstrability of a system. The
direct involvement of management in the modification of system features, incentive structures,
and work processes will reduce anxiety related to the impact and use of the system and, hence,
will influence the determinants of perceived ease of use such as perceptions of external control.
While management support has been conceptualized and operationalized as
organizational mandate and compliance, particularly in the individual-level IT adoption
literature, we suggest that there is a need to develop a richer conceptualization of management
support to enhance our understanding of its role in IT adoption contexts. We suggest that social
network theory and analysis (e.g., Burkhardt & Brass, 1990; Burt, 1992), and leader-member
exchange (LMX) theory (e.g., Liden, Sparrowe, & Wayne, 1997) can be used to understand the
influence of management supports in IT adoption and use. Social network analysis can help
pinpoint the mechanisms through which management supports can influence the determinants of
perceived usefulness and perceived ease of use. Examples of research questions are:
i. What forms of management support (e.g., indirect or direct actions) are important in creating
favorable perceptions toward a new system?
ii. What are the effective modes of managerial communication to express support toward a new
system?
iii. How does organizational mandate differ from managerial support? Which one of these is
more effective for complex systems implementations?
29
Incentive alignment
Incentive alignment has been suggested as the third dimension in systems design (Ba, Stallaert,
& Whinston, 2001). The other two dimensions are software engineering and technology
acceptance (Ba et al., 2001). Ba et al. (2001) argued that while aspects of software engineering
(e.g., system characteristics) and technology acceptance (e.g., perceived usefulness, perceived
ease of use, user satisfaction) are important considerations for system development processes,
organizations may fail to gain expected benefits from employees’ effective utilization of a
system unless employees’ find that the system features and capabilities are aligned with their
interests and incentives. For example, even if a system is of high quality from a system
engineering point of view and users may develop positive attitudes toward the system from a
technology acceptance point of view, it may not lead to positive organizational outcomes if there
are no incentives in place for the users for using the system effectively. While there have been
many examples of IT failures because of a lack of incentive alignment, there is little or no
research on the role of incentive alignment in IT adoption contexts. However, in decision support
systems and group support systems use contexts (e.g., Mennecke & Valacich, 1998; Speier et al.,
2003), incentive has been found to be an important factor (see Todd & Benbasat, 1999). We
suggest that incentive alignment can be an important intervention in the pre-implementation
stage that can potentially enhance user acceptance. According to Ba et al. (2001), incentive
alignment does not necessarily mean organizational rewards for using a system. It is a broad
concept that entails an individual’s perception that the IT fits with his or her job requirements
and value system. For example, in the context of enterprise systems, if an individual perceives
that his or her use of the system does not benefit the members of his or her work units but rather
benefits members from other work units, the user will perceive a lack of incentive alignment that
30
may lead to low user acceptance and use of the system. Incentive alignment can potentially
influence employees’ perceptions of job relevance, output quality, and results demonstrability of
a system. Given that their use of the system will be noticed and rewarded by the management,
incentives can influence subjective norm and image. Further, incentive alignment, as an
important extrinsic reward, may reduce anxiety and increase perceived enjoyment as extrinsic
rewards are considered important drivers of intrinsic motivations (Deci, Koestner, & Ryan, 1999;
Ryan & Deci, 2000; Vallerand, 1997).
We believe that there can be many fruitful avenues of research on the role of incentive
alignment in the context of IT adoption. Two examples of relevant research questions are:
i. What is the role of incentive alignment in determining perceived usefulness and perceived ease
of use of a system?
ii. How can organizational incentive structure be incorporated in the configuration of a complex
system? How does such incorporation enhance user acceptance of such systems?
Post-implementation Interventions
Post-implementation interventions represent a set of organizational, managerial and support
activities that take place after the deployment of a system to enhance the level of user acceptance
of the system. While pre-implementation interventions are designed and implemented in order to
reduce initial resistance and develop realistic perceptions of system features, capabilities, and
relevance, post-implementation interventions can be crucial to help employees go through the
initial shock and changes associated with the new system. When employees start using a new
system, as noted earlier, they are more likely to experience substantial changes to their intrinsic
job characteristics, work processes, routines, and habits (Millman & Hartwick, 1987). Some
employees may react favorably to these changes, while the others may perceive these changes as
31
a threat to their well-being (Boudreau & Robey, 2005; Orlikowski, 2000). During post-
implementation stages, employees attempt to cope with the new system in different ways
depending on whether they perceive the system as a threat (or an opportunity) and whether they
have control over the system (Beaudry & Pinsonneault, 2005). For example, if employees
perceive that a new system is a threat to their well-being and they do not have necessary
resources and abilities to use the system, it is more likely that they will try to avoid the new
system (Beaudry & Pinsonneault, 2005). In keeping with this, post-implementation interventions
should make employees feel that a new system is an opportunity to enhance their job
performance and they have abilities and necessary resources to use the new system effortlessly.
Training
Training has been suggested as one of the most important post-implementation interventions that
leads to greater user acceptance and system success (see Sharma & Yetton, 2007). While training
can be conducted before or during implementation of a new system, we consider training as a
post-implementation intervention because, in most cases, training is conducted after a system is
deployed and ready to be used by the potential users. Much prior research has suggested the
critical role of training in enhancing IT adoption and use (e.g., Venkatesh, 1999; Venkatesh &
Speier, 1999; Wheeler & Valacich, 1996). One of the key reasons for training to be an important
intervention is that different modes of training can be used to manipulate different determinants
of IT adoption. For example, Venkatesh (1999) found that game-based training was more
effective than traditional training to enhance user acceptance of a new system. He also found that
the effect of perceived ease of use on behavioral intention to use a system was stronger for
individuals who received game-based training. Venkatesh and Speier (1999) investigated the
effect of mood during training on user acceptance and found that mood during training played an
32
important role in forming individuals perceptions of a new IT. These findings indicate that
training can be used to help users develop favorable perceptions of different determinants of
perceived usefulness and perceived ease of use. However, much of the prior research on training
in the context of IT adoption has been conducted for simple ITs, such as word processing system
and e-mail system. We suggest that the role of training will be even more important in the
context of complex, enterprise systems that are more central to employees’ work life. As these
systems are more likely to invoke negative reactions from employees because of their disruptive
nature, effective training interventions can mitigate these negative reactions and help employees
form favorable perceptions toward these systems.
The research on modes and effectiveness of training in the context of IT use is rich (e.g.,
Davis & Bostrom, 1993; Davis & Yi, 2004; Venkatesh, 1999; Venkatesh & Speier, 1999). But
there is a still a need for understanding at a more specific level of different training modes and
their effects on the determinants of IT adoption. Some examples of research questions are:
i. Which training method is the most effective for enhancing the determinants of perceived
usefulness and perceived ease of use?
ii. To achieve greater user acceptance, when is the appropriate time for trainingearly in the
development stage or later part of the development?
iii. Should there be separate training for business processes during the implementation of
complex systems that require business process changes? How and why does training on business
process influence user acceptance of these technologies?
Organizational support
Organizational support refers to informal or formal activities or functions to assist employees in
using a new system effectively. Organizations can provide support in various formsproviding
33
necessary infrastructure, creating dedicated help desks, hiring system and business process
experts, and sending employees to off-the-job training. In the post-implementation stage, the
presence of different types support is very important, particularly in the context of complex,
enterprise systems that are inherently difficult to understand and use (e.g., Bajwa, Rai, &
Brennan, 1998). Prior research has suggested that employees’ perceptions regarding
organizational supportthat is, facilitating conditions or perceptions of external control (Taylor
& Todd, 1995; Venkatesh, 2000; Venkatesh et al., 2003)will lead to greater user acceptance of
new systems. Jasperson et al. (2005) noted the importance of internal or external experts as
sources of interventions. Organizational support captures the role of both internal and external
experts who can help users deal with the complexity associated with new systems as well as
business processes. These experts can help users modify or enhance the IT applications or work
processes (Jasperson et al., 2005). Thus, organizational support can play a key role in
determining perceived usefulness and perceived ease of use. For example, experts can help
employees modify certain aspects of a new system, thus increasing job relevance, output quality,
and results demonstrability of system. TAM3 posits that perceptions of external control are
important and stable determinants of perceived ease of use. Organizational support is a key
source of perceptions of external control. Further, the presence of organizational support,
particularly in the context of complex systems, can reduce anxiety associated with system use.
While the notion of organizational support has been captured in the IT adoption literature
through facilitating conditions and/or perceptions of external control, we suggest that it is
important to understand the specific role of different types of organizational support that may
influence different determinants of perceived usefulness and perceived ease of use. Examples of
research questions are:
34
i. How should organizational support structure be designed for complex, enterprise systems that
require both technology and domain-specific business process knowledge for the users and
support personnel?
ii. How and why do different forms of organizational support (e.g., infrastructure, help desks,
system and business process experts, and off-the-job training) influence the determinants of
perceived usefulness and perceived ease of use?
Peer support
Peer support refers to different activities and/or functions performed by coworkers that may help
an employee to effectively use a new system. Jasperson et al. (2005) suggested that coworkers
from the same or different business units and workers in other organizations can be important
sources of interventions leading to greater user acceptance of a system. They suggested three
intervention actions related to peers: (i) formal or informal training; (ii) direct modification or
enhancement of IT system or work processes; and (iii) joint (with users) modification or
enhancement of work processes. We suggest that these interventions can influence the
determinants of perceived usefulness and perceived ease of use in several ways. First, peer
support through formal and informal training can enhance users’ understanding of the system.
Thus, users may get insights from their peers on job relevance, output quality, and result
demonstrability of a system. Second, the modification and enhancement activities performed by
peers will increase job relevance of the system, improve the output quality of the system, and
reduce anxiety related to system use. Finally, peer support may also influence subjective norm
and image associated with using a system. If coworkers are favorable toward a new system, it is
more likely that employees will form favorable perceptions toward the system through social
influence processes (Venkatesh and Davis, 2000).
35
While peer support is potentially an important intervention that can lead to greater user
acceptance, there is little or no research on the role of peer support in the context of IT adoption.
We urge IS researchers to investigate how peer support can enhance user acceptance by
influencing the determinants of perceived usefulness and perceived ease of use. We believe that
social network theory and analysis, and team member exchange (TMX) theory (e.g., Seers, 1989)
can be used to understand the influence of peer support in IT adoption and use. Some research
questions are:
i. How and why does peer support enhance perceived usefulness and perceived ease of use of a
system? Does peer support have a differential influence on perceived usefulness and perceived
ease of use in different cultural contexts (e.g., Straub, Keil, & Brenner, 1997)?
ii. What types of intervention actions related to peer support are more effective in enhancing
perceived usefulness and perceived ease of use for complex systems?
DISCUSSION
We had three objectives in this research: (i) developing a comprehensive nomological network
(integrated model) of the determinants of individual level adoption and use; (ii) empirical testing
the proposed integrated model; and (iii) presenting a research agenda focused on potential pre-
and post-implementation interventions that could enhance employees’ adoption and use of IT. To
accomplish our first objective, we integrated the models proposed by Venkatesh and Davis
(2000) and Venkatesh (2000) and developed a comprehensive nomological network of IT
adoption and useTAM3. We accomplished the second objective by testing the integrated
model through longitudinal field studies conducted at four different organizations. Finally, we
accomplished the third objective by presenting a set of interventions and an agenda of future
research on these interventions. We discussed how and why these interventions may influence
36
the determinants of perceived usefulness and perceived ease of use.
Theoretical Contributions
Our research makes several important theoretical contributions. We present a complete
nomological network of the determinants of IT adoption and useTAM3. The key strength of
TAM3 is its comprehensiveness and potential for actionable guidance. While TAM presented a
parsimonious model, the follow-up research on the general determinants of perceived usefulness
and perceived ease of use presented pointers to constructs that could be levers. The current work
adds richness and insights to our understanding of user reactions to new ITs in the workplace.
Comprehensiveness and parsimony have their own merits in theory development (e.g.,
Bacharach, 1989; Dubin, 1976; Whetten, 1989). While comprehensiveness ensures whether all
relevant factors are included in a theory, parsimony dictates whether some factors should be
deleted because they add little value to our understanding of a phenomenon (Whetten, 1989). We
suggest that the comprehensiveness of TAM3 is important as we now move more toward a
research agenda related to various interventions.
TAM3 emphasizes the unique role and processes related to perceived usefulness and
perceived ease of use and theorizes that the determinants of perceived usefulness will not
influence perceived ease of use and vice versa. This is an important theoretical contribution by
itself because there have been many inconclusive findings regarding the relationships among
some of these determinants, perceived usefulness, and perceived ease of use. For example,
Agarwal and Karahanna (2000) found that computer self-efficacy was a significant determinant
of perceived usefulness. However, Venkatesh (2000) found that perceived ease of use fully
mediated the effect of computer self-efficacy on behavioral intention. We provided the
theoretical justification and empirical support of why the determinants of perceived ease of use
37
(e.g., computer self-efficacy) will not have significant effects on perceived usefulness over and
above the known determinants of perceived usefulness that are driven by the social influence and
cognitive instrumental processes. For example, while self-efficacy may have weak influence on
perceived usefulness as shown in Agarwal and Karahanna (2000), we argue that this influence
will become non-significant in the presence of other important social and cognitive constructs.
TAM3 posits new theoretical relationships such as the moderating effects of experience
on key relationships. Experience is an important moderating variable in IT adoption contexts
because, as suggested in much prior research, individuals’ reactions toward an IT may change
over time (Bhattacherjee & Premkumar, 2004; Karahanna et al., 1999). The changing
perceptions can play an important role in determining individuals’ continuance intention and
long-term use of a system (Bhattacherjee, 2001). While initial adoption is important, long-term
use of a system is a key measure of ultimate success of a system (DeLone & McLean, 2003; Rai
et al., 2002). Therefore, it is important to understand the role of experience in IT adoption and
use contexts (Venkatesh et al., 2003). TAM3 posits that with increasing experience, while the
effect of perceived ease of use on behavioral intention diminished, the effect of perceived ease of
use on perceived usefulness increased. This clearly indicates that perceived ease of use is still an
important user reaction toward IT even if users have substantial hands-on experience with the IT.
This important theoretical relationship has significant practical utility as there has been
increasing concerns about the ease of use of various ITs, particularly enterprise systems that are
inherently complex to understand and use. There have been numerous cases of enterprise system
failures because of user resistance. In many cases, the users stopped using an enterprise system
as they saw no benefits of using the new system. It is possible that a lack of perceived ease of use
contributed to unfavorable perceptions of perceived usefulness in the context of those systems.
38
Finally, our most important theoretical contribution is the delineation of relationships
among the suggested interventions and the determinants of perceived usefulness and perceived
ease of use. While prior research (e.g., Venkatesh, 1999) has suggested important relationships
between interventions (e.g., training) and key IT adoption determinants, we extend this research
by providing a comprehensive list of interventions, suggesting potential relationships of these
interventions with the determinants of perceived usefulness and perceived ease of use, and
offering important future research directions. Our key argument in this paper is that unless
organizations can develop effective interventions to enhance IT adoption and use, there is no
practical utility of our rich understanding of IT adoption. However, there is little or no scientific
research aimed at identifying and linking interventions with specific determinants of IT adoption.
The importance of interventions in enhancing IT adoption was underscored by Venkatesh (1999)
who provided an example of how different modes of training can be used to manipulate system-
specific enjoyment which enhanced the salience perceived ease of use of a system as a
determinants of behavioral intention. Our theoretical arguments about the relationships between
the interventions and the determinants of IT adoption are thus an important contribution that
could direct future research.
Implications for Decision Making
We suggest that our findings and research agenda focusing on interventions have direct
implications for two types of decision making in organizations(i) employees’ IT adoption
decisions; and (ii) managerial decisions about managing IT implementation process. Further,
given that ITs are becoming increasingly complex and pertinent to employees decision making
and work processes, this research has implications for broad IT-enabled organizational decision
making (e.g., collaborative forecasting, inventory management, replenishment, service delivery).
39
Our discussion of interventions primarily focuses on these complex ITs to understand how pre-
and post-implementation interventions can help employees make better adoption decisions about
these complex systems and managers make effective implementation decisions. This is
consistent with Venkatesh (2006) who argued that in order to be relevant to organizational
decision-making processes, individual-level IT adoption research should focus on phenomena
that are pertinent to decision making (e.g., knowledge sharing, business process outsourcing) and
ITs that are critical for organizational decision making (e.g., enterprise resource planning, supply
chain management, collaborative forecasting, inventory management systems). The interventions
and future research agenda discussed here have implications for these types of phenomena and
systems.
Due to the complexity of ITs, it is increasingly difficult for employees to make effective
decisions about adoption, utilization, and coping with new IT. As discussed earlier,
implementation of complex ITs (e.g., enterprise systems, interorganizational systems) and
associated changes in business processes have a profound impact on employees job and cause
changes in their job characteristics, relationships with others in the workplace, and other aspects
of their job (Boudreau & Robey, 2005; Lapointe & Rivard, 2005). Consequently, employees’ job
outcomes (e.g., job satisfaction and job performance) can be affected. Due to the magnitude of
these impacts, employees are reluctant to adopt new ITs (Lapointe & Rivard, 2005). Other types
of reactions, such as avoidance, sabotage, workarounds, and shortcuts are also prevalent.
Interventions that we discuss here can help employees make appropriate decisions about
adopting and utilizing a new IT. For example, in the context of enterprise systems, certain design
characteristics (e.g., extent of customization or complexity of the system) can reduce changes in
employees’ jobs as these characteristics can potentially enhance the fit between a system and
40
employees’ jobs. Some other interventions (e.g., user participation, training) can help employees
decide how to cope with or adapt a new IT (Beaudry & Pinsonneault, 2005). Venkatesh (2006)
called for work on employees reactions to business process changes and process standards
implementation. We suggest that interventions discussed in this paper can help organizations
generate favorable individuals’ reactions toward business process changes and process standards
implementation.
Our findings and discussion of interventions can support managerial decision making in two
ways. First, managers will now have a framework to decide what interventions to apply during
pre- and post-implementation stages and for what types of system. For instance, (i) for a complex
system, perhaps, interventions that will create favorable ease of use perceptions will be relevant
(e.g., design characteristics, user participation, training, and peer support); (ii) for a voluntary
system, interventions that will influence the determinants of perceived usefulness will be
important to implement (e.g., design characteristics, user participation, incentive alignment,
training, organizational and peer support); and (iii) for interorganizational systems that affect
organizational business processes (e.g., Saeed, Malhotra, & Grover, 2005) or a customer
relationship management system that is critical to service delivery (e.g., Froehle, 2006),
interventions, such as user participation, peer support, and management support will be
particularly relevant. Second, managers can decide on resource allocation for interventions based
on the impact of interventions on different determinants of IT adoption and type of systems. For
example, if design characteristics cannot be changed in a system, managers can allocate more
resources to training and user participation to make employees familiar with the systems. The
implementation of interventions is, of course, not a silver bullet for greater IT adoption and
effective utilization. Implementation of interventions can increase system development costs
41
substantially. Hence, managers have to be mindful in their decisions about implementing
interventions and our work indentifies specific interventions that can serve as levers for
managers that can be helpful in their decision-making processes.
CONCLUSIONS
ITs are becoming increasingly complex and implementation costs are very high. Implementation
failures of many of today’s ITs cost millions of dollars for organizations. Further, low adoption
and high underutilization of ITs have been a major problem for organizations in terms of
realizing the benefits (both tangible and intangible) of IT implementations (Jasperson et al.,
2005). If we can develop a rich understanding of the determinants of IT adoption and use and
interventions that can favorably influence these determinants, managers can proactively decide
on implementing the right interventions to minimize resistance to new ITs and maximize
effective utilization of ITs. Based on a comprehensive nomological network of IT adoption and
useTAM3we presented a set of pre- and post-implementation interventions that we believe
should be the object of future scientific inquiry.
42
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51
APPENDIX A: ITEMS FOR TAM3 CONSTRUCTS
Constructs
Items
Perceived
Usefulness (PU)
PU1
Using the system improves my performance in my job.
PU2
Using the system in my job increases my productivity.
PU3
Using the system enhances my effectiveness in my job.
PU4
I find the system to be useful in my job.
Perceived Ease
of Use (PEOU)
PEOU1
My interaction with the system is clear and understandable.
PEOU2
Interacting with the system does not require a lot of my mental
effort.
PEOU3
I find the system to be easy to use.
PEOU4
I find it easy to get the system to do what I want it to do.
Computer Self-
Efficacy (CSE)
I could complete the job using a software package. . .
CSE1
. . . if there was no one around to tell me what to do as I go.
CSE2
. . . if I had just the built-in help facility for assistance.
CSE3
. . . if someone showed me how to do it first.
CSE4
. . . if I had used similar packages before this one to do the same job.
Perceptions of
External
Control (PEC)
PEC1
I have control over using the system.
PEC2
I have the resources necessary to use the system.
PEC3
Given the resources, opportunities and knowledge it takes to use the
system, it would be easy for me to use the system.
PEC4
The system is not compatible with other systems I use.
Computer
Playfulness
(CPLAY)
The following questions ask you how you would characterize
yourself when you use computers:
CPLAY1
. . . spontaneous
CPLAY2
. . . creative
CPLAY3
. . . playful
CPLAY4
. . . unoriginal
Computer
Anxiety
(CANX)
CANX1
Computers do not scare me at all.
CANX2
Working with a computer makes me nervous.
CANX3
Computers make me feel uncomfortable.
CANX4
Computers make me feel uneasy.
Perceived
Enjoyment
(ENJ)
ENJ1
I find using the system to be enjoyable.
ENJ2
The actual process of using the system is pleasant.
ENJ3
I have fun using the system.
Objective
Usability (OU)
No specific items were used. It was measured as a ratio of time spent
by the subject to the time spent by an expert on the same set of tasks.
Subjective
Norm (SN)
SN1
People who influence my behavior think that I should use the
system.
SN2
People who are important to me think that I should use the system.
SN3
The senior management of this business has been helpful in the use
of the system.
SN4
In general, the organization has supported the use of the system.
Voluntariness
VOL1
My use of the system is voluntary.
52
(VOL)
VOL2
My supervisor does not require me to use the system.
VOL3
Although it might be helpful, using the system is certainly not
compulsory in my job.
Image (IMG)
IMG1
People in my organization who use the system have more prestige
than those who do not.
IMG2
People in my organization who use the system have a high profile.
IMG3
Having the system is a status symbol in my organization.
Job Relevance
(REL)
REL1
In my job, usage of the system is important.
REL2
In my job, usage of the system is relevant.
REL3
The use of the system is pertinent to my various job-related tasks.
Output Quality
(OUT)
OUT1
The quality of the output I get from the system is high.
OUT2
I have no problem with the quality of the system’s output.
OUT3
I rate the results from the system to be excellent.
Result
Demonstrability
(RES)
RES1
I have no difficulty telling others about the results of using the
system.
RES2
I believe I could communicate to others the consequences of using
the system.
RES3
The results of using the system are apparent to me.
RES4
I would have difficulty explaining why using the system may or may
not be beneficial.
Behavioral
Intention (BI)
BI1
Assuming I had access to the system, I intend to use it.
BI2
Given that I had access to the system, I predict that I would use it.
BI3
I plan to use the system in the next <n> months.
Use (USE)
USE1
On average, how much time do you spend on the system each day?
a. All items were measured on a 7-point Likert scale (where 1: strongly disagree; 2: moderately
disagree, 3: somewhat disagree, 4: neutral (neither disagree nor agree), 5: somewhat agree, 6:
moderately agree, and 7: strongly agree), except computer self-efficacy which was measured
using a 10-point Guttman scale.
53
TABLES AND FIGURES
Table 1: Determinants of perceived usefulness
Determinants
Definitions
Perceived ease of use
The degree to which a person believes that using an IT will be free of
effort (Davis et al., 1989).
Subjective norm
The degree to which an individual perceives that most people who are
important to him think he should or should not use the system
(Fishbein & Ajzen, 1975; Venkatesh & Davis, 2000).
Image
The degree to which an individual perceives that use of an innovation
will enhance his or her status in his or her social system (Moore &
Benbasat, 1991).
Job relevance
The degree to which an individual believes that the target system is
applicable to his or her job (Venkatesh & Davis, 2000).
Output quality
The degree to which an individual believes that the system performs
his or her job tasks well (Venkatesh & Davis, 2000).
Result demonstrability
The degree to which an individual believes that the results of using a
system are tangible, observable, and communicable (Moore &
Benbasat, 1991).
54
Table 2: Determinants of perceived ease of use
Determinants
Definitions
Computer self-efficacy
The degree to which an individual believes that he or she has the
ability to perform a specific task/job using computer (Compeau &
Higgins, 1995a, 1995b).
Perception of external
control
The degree to which an individual believes that organizational and
technical resources exist to support use of the system (Venkatesh et
al., 2003).
Computer anxiety
The degree of “an individual’s apprehension, or even fear, when
she/he is faced with the possibility of using computers” (Venkatesh,
2000, p. 349).
Computer playfulness
“…the degree of cognitive spontaneity in microcomputer
interactions” (Webster & Martocchio, 1992, p. 204).
Perceived enjoyment
The extent to which “the activity of using a specific system is
perceived to be enjoyable in its own right, aside from any
performance consequences resulting from system use” (Venkatesh,
2000, p. 351).
Objective usability
A “comparison of systems based on the actual level (rather than
perceptions) of effort required to complete specific tasks”
(Venkatesh, 2000, pp. 350-351).
55
Table 3: Items loadings from PLS (N=156 at each time period)
Constructs
Items
T1
T2
T3
Pooled
Constructs
Items
T1
T2
T3
Pooled
Perceived
Usefulness
(PU)
PU1
.88
.84
.90
.88
Subjective
Norm (SN)
SN1
.84
.88
.80
.83
PU2
.84
.88
.90
.89
SN2
.88
.82
.75
.78
PU3
.90
.90
.89
.90
SN3
.80
.77
.75
.77
PU4
.92
.91
.94
.92
SN4
.80
.78
.70
.76
Perceived
Ease of Use
(PEOU)
PEOU1
.90
.89
.88
.90
Voluntariness
(VOL)
VOL1
.77
.84
.88
.85
PEOU2
.90
.92
.92
.91
VOL2
.85
.90
.92
.88
PEOU3
.93
.90
.90
.91
VOL3
.83
.85
.90
.88
PEOU4
.94
.93
.92
.93
Image (IMG)
IMG1
.82
.85
.88
.85
Computer
Self-Efficacy
(CSE)
CSE1
.84
.80
.77
.80
IMG2
.86
.78
.79
.82
CSE2
.78
.75
.70
.74
IMG3
.90
.92
.90
.90
CSE3
.73
.73
.72
.72
Job Relevance
(REL)
REL1
.91
.84
.85
.90
CSE4
.74
.71
.73
.72
REL2
.88
.90
.81
.89
Perceptions
of External
Control
(PEC)
PEC1
.80
.77
.75
.76
REL3
.84
.84
.80
.82
PEC2
.78
.77
.73
.74
Output Quality
(OUT)
OUT1
.90
.88
.84
.88
PEC3
.77
.74
.74
.74
OUT2
.83
.80
.70
.79
PEC4
.75
.75
.73
.73
OUT3
.77
.72
.74
.72
Computer
Playfulness
(CPLAY)
CPLAY1
.74
.78
.79
.77
Result
Demonstrability
(RES)
RES1
.80
.82
.84
.80
CPLAY2
.74
.77
.70
.72
RES2
.83
.80
.70
.77
CPLAY3
.73
.74
.73
.74
RES3
.82
.80
.72
.75
CPLAY4
.80
.84
.70
.78
RES4
.73
.72
.80
.71
Computer
Anxiety
(CANX)
CANX1
.77
.70
.74
.73
Behavioral
Intention (BI)
BI1
.80
.82
.84
.82
CANX2
.70
.74
.75
.74
BI2
.90
.92
.90
.92
CANX3
.73
.70
.77
.75
BI3
.90
.88
.84
.87
CANX4
.76
.76
.74
.74
Use (USE)
USE1
1.00
1.00
1.00
1.00
Perceived
Enjoyment
(ENJ)
ENJ1
.85
.88
.82
.84
ENJ2
.84
.85
.82
.80
ENJ3
.80
.84
.84
.83
a. The loadings at T1, T2, T3, and pooled respectively are from separate measurement model
tests.
All cross-loadings were below .30.
56
Table 4: Measurement model estimation at three time periods (N=156 at each time period)
(a) T1 results
M
SD
ICR
PU
PEOU
CSE
PEC
CPLAY
CANX
ENJ
OU
SN
IMG
JREL
OUT
RES
BI
USE
PU
4.14
1.22
.92
.83
PEOU
3.98
1.07
.93
.30***
.87
CSE
4.66
1.33
.80
.17*
.40***
.77
PEC
3.98
1.27
.76
.15*
.36***
.29***
.74
CPLAY
4.41
1.09
.82
.08
.35***
.33***
.17*
.74
CANX
3.88
1.23
.83
-.14*
-.38***
-.20*
-.19*
-.33***
.72
ENJ
3.22
1.07
.88
.07
.22**
.08
.10
.18*
-.19*
.82
OU
NA
NA
NA
.15*
.18*
.11
.04
.08
.08
.03
NA
SN
4.87
1.22
.85
.30***
.19*
-.14*
.16*
-.17*
.20**
.10
.08
.81
IMG
3.94
1.45
.83
.26***
.08
.18*
.08
.13
.18*
.14
.09
.43***
.82
JREL
4.01
1.32
.83
.32***
.23***
.16*
.18*
.02
.12
.10
.03
.22***
.11
.78
OUT
4.08
1.22
.77
.28***
.24***
.09
.04
.09
.02
.04
.08
.16*
.20**
.32***
.76
RES
3.56
1.09
.85
.28***
.17*
.04
.09
.00
.05
.10
.08
.25***
.14*
.16*
.27***
.71
BI
4.10
1.35
.90
.59***
.30***
.22***
.26***
.18*
-.19*
.17*
.17*
.17*
.26***
.27***
.26***
.26***
.85
USE
7.85
3.33
NA
.51***
.27***
.18*
.24***
.16*
-.17*
.16*
.17*
.23***
.24***
.22**
.22**
.21**
.57***
NA
(b) T2 results
M
SD
ICR
PU
PEOU
CSE
PEC
CPLAY
CANX
ENJ
OU
SN
IMG
JREL
OUT
RES
BI
USE
PU
4.41
1.21
.94
.85
PEOU
4.43
1.04
.90
.32***
.85
CSE
4.72
1.30
.82
.16*
.41***
.75
PEC
4.28
1.20
.73
.17*
.37***
.30***
.73
CPLAY
4.36
1.11
.81
.07
.38***
.30***
.19**
.76
CANX
4.01
1.28
.84
-.18*
-.29***
-.22**
-.18*
-.30***
.71
ENJ
3.85
1.22
.89
.09
.27***
.16*
.08
.19**
-.20**
.82
OU
NA
NA
NA
.22**
.24***
.14*
.02
.03
-.09
-.19*
NA
SN
4.56
1.30
.83
.25***
.17*
-.17*
.15*
-.19**
.18*
.08
.04
.80
IMG
4.28
1.40
.81
.29***
.08
.20**
.03
.10
.16*
.10
.03
.40***
.82
JREL
4.29
1.36
.85
.29***
.25***
.18*
.19*
.03
.10
.10
.05
.18**
.16*
.80
OUT
4.33
1.08
.75
.23***
.21**
.04
.02
.07
.04
.05
.07
.19**
.22**
.27***
.76
RES
3.87
1.23
.84
.32***
.16*
.04
.08
.02
.04
.07
.10
.23***
.10
.14*
.26***
.72
BI
4.41
1.51
.91
.59***
.24***
.21**
.26***
.17*
-.17*
.17*
.19**
.12
.12
.24***
.23***
.22**
.80
USE
11.23
4.29
NA
.50***
.22***
.18*
.24***
.15*
-.16*
.17*
.17*
.17*
.25***
.22**
.24***
.20**
.56***
NA
57
(c) T3 results
M
SD
ICR
PU
PEOU
CSE
PEC
CPLAY
CANX
ENJ
OU
SN
IMG
JREL
OUT
RES
BI
USE
PU
4.55
1.27
.94
.84
PEOU
4.89
1.13
.93
.38***
.88
CSE
4.70
1.28
.85
.15*
.44***
.78
PEC
4.51
1.28
.78
.19**
.47***
.05
.75
CPLAY
4.40
1.20
.84
.10
.28***
.29***
.20**
.75
CANX
4.10
1.35
.84
-.20**
-.25***
-.22**
-.19**
-.24***
.76
ENJ
4.13
1.28
.89
.05
.30***
.07
.09
.18*
-.20**
.83
OU
NA
NA
NA
.26***
.27***
18*
.17*
.10
-.17*
.16*
NA
SN
4.28
1.25
.86
.25***
.23***
-.14*
.18*
-.16*
.17*
.04
.03
.82
IMG
4.44
1.23
.84
.25***
.08
.22**
.04
.04
.16*
.07
.05
.41***
.83
JREL
4.39
1.29
.82
.32***
.22**
.16*
.17*
.02
.05
.02
.07
.24***
.17*
.81
OUT
4.49
1.20
.76
.28***
.20**
.02
.03
.01
.02
.04
.08
.23***
.20**
.28***
.79
RES
4.10
1.09
.85
.30***
.15*
.06
.07
.05
.03
.05
.04
.20**
.15*
.10
.27***
.73
BI
4.54
1.33
.88
.58***
.19**
.20**
.24***
.16*
-.18*
.16*
.17*
.17*
.24***
.22***
.23***
.23***
.81
USE
12.87
5.13
NA
.49***
.17*
.18*
.21**
.15*
-.15*
.17*
.16*
.17*
.22***
.18*
.20**
.21**
.59***
NA
a. ICR: Internal consistency reliability; Diagonal elements are the square root of the shared variance between the constructs and their
measures; off-diagonal elements are correlations between constructs.
b. PU: Perceived Usefulness; PEOU: Perceived Ease of Use; CSE: Computer Self-Efficacy; PEC: Perceptions of External Control;
CPLAY: Computer Playfulness; CANX: Computer Anxiety; ENJ: Perceived Enjoyment; OU: Objective Usability; SN: Subjective
Norm; IMG: Image; REL: Job Relevance; OUT: Output Quality; RES: Result Demonstrability; BI: Behavioral Intention; USE: Use.
c. * p < 0.05, ** p < 0.01, *** p < 0.001.
58
Table 5: Explaining perceived usefulness
T1 (N=156)
T2 (N=156)
T3 (N=156)
Pooled (N=468)
R2
.60
.56
.52
.67
Perceived Ease of Use (PEOU)
.22***
.26***
.33***
.08
Subjective Norm (SN)
.40***
.32***
.13*
.04
Image (IMG)
.27***
.20**
.23***
.24***
Job Relevance (REL)
.04
.05
.08
.03
Output Quality (OUT)
.06
.01
.02
.03
Result Demonstrability (RES)
.22***
.26***
.28***
.26***
Computer Self-Efficacy (CSE)
.07
.03
.01
.04
Perceptions of Ext. Control (PEC)
.04
.01
.04
.03
Computer Anxiety (CANX)
.03
.04
.02
.03
Computer Playfulness (PLAY)
.08
.02
.05
.04
Perceived Enjoyment (ENJ)
.02
.05
.02
.04
Objective Usability (OU)
.01
.00
.00
.01
Experience (EXP)
.03
EOU X EXP
.39***
SN X EXP
-.29***
REL X OUT
.37***
.34***
.35***
.35***
a. Shaded areas are not applicable for the specific column.
b. * p < 0.05, ** p < 0.01, *** p < 0.001.
59
Table 6: Explaining perceived ease of use
T1 (N=156)
T2 (N=156)
T3 (N=156)
Pooled (N=468)
R2
.43
.45
.44
.52
Subjective Norm (SN)
.03
.01
.04
.04
Image (IMG)
.04
.04
.00
.00
Job Relevance (REL)
.02
.01
.05
.05
Output Quality (OUT)
.05
.04
.07
.07
Result Demonstrability (RES)
.02
.03
.02
.02
Computer Self-Efficacy (CSE)
.35***
.30***
.28***
.31***
Perceptions of Ext. Control (PEC)
.37***
.30***
.30***
.33***
Computer Anxiety (CANX)
-.22***
-.18**
-.14*
-.18**
Computer Playfulness (CPLAY)
.20**
.16*
.11*
.15**
Perceived Enjoyment (ENJ)
.02
.22***
.24***
.04
Objective Usability (OU)
.04
.19**
.23***
.03
Experience (EXP)
.01
CPLAY X EXP
-.22***
CANX X EXP
.21***
ENJ X EXP
.18**
OU X EXP
.20**
a. Shaded areas are not applicable for the specific column.
b. * p < 0.05, ** p < 0.01, *** p < 0.001.
60
Table 7: Explaining behavioral intention (BI)
T1 (N=156)
T2 (N=156)
T3 (N=156)
Pooled (N=468)
R2
.48
.44
.40
.53
Perceived Usefulness (PU)
.55***
.56***
.57***
.56***
Perceived Ease of Use (PEOU)
.24***
.17*
.05
.04
Subjective Norm (SN)
.03
.04
.02
.02
Experience (EXP)
.02
Voluntariness (VOL)
.02
.02
.04
.07
PEOU X EXP
-.24***
SN X EXP
.04
SN X VOL
.29***
.22***
.17*
.03
SN X EXP X VOL
-.46***
a. Shaded areas are not applicable for the specific column.
b. * p < 0.05, ** p < 0.01, *** p < 0.001.
61
Table 8: Explaining use
T2 (N=156)
T3 (N=156)
T4 (N=156)
Pooled (N=468)
R2
.32
.31
.36
.35
Behavioral Intention (BI)
.57***
.56***
.60***
.59***
a. * p < 0.05, ** p < 0.01, *** p < 0.001.
62
Table 9: Summary of interventions
Pre-implementation Interventions
Post-implementation
Interventions
Design
Characteristics
User
Participation
Management
Support
Incentive
Alignment
Training
Organizational
Support
Peer
Support
Determinants of Perceived Usefulness
Subjective
Norm
X
X
X
X
Image
X
X
X
Job Relevance
X
X
X
X
X
X
X
Output Quality
X
X
X
X
X
X
X
Result
Demonstrability
X
X
X
X
X
X
X
Determinants of Perceived Ease of Use
Computer Self-
Efficacy
X
Perceptions of
Ext. Control
X
X
X
X
Computer
Anxiety
X
X
X
Computer
Playfulness
X
X
Perceived
Enjoyment
X
X
X
X
Objective
Usability
X
X
X
a. X indicates a particular intervention can potentially influence a particular determinants of
perceived usefulness or perceived ease of use.
63
Technology Acceptance Model (TAM)
Behavioral
Intention
Individual
Differences
System
Characteristics
Social Influence
Use
Behavior
Facilitating
Conditions
Figure 1: Theoretical framework
Perceived
Usefulness
Perceived
Ease of Use
64
Figure 2: Technology acceptance model 3 (TAM3)
a. Thick lines indicate new relationships proposed in TAM3.
Technology Acceptance Model (TAM)
Adjustment
Anchor
Perceived
Usefulness
Perceived
Ease of Use
Behavioral
Intention
Subjective Norm
Image
Output Quality
Job Relevance
Result
Demonstrability
Experience
Voluntariness
Computer Self-
efficacy
Perceptions of
External Control
Computer
Anxiety
Computer
Playfulness
Perceived
Enjoyment
Objective
Usability
Use
Behavior
65
ABOUT THE AUTHORS
Viswanath Venkatesh
Viswanath Venkatesh is a professor and the George and Boyce Billingsley Chair in Information
Systems at the Walton College of Business, University of Arkansas. His research focuses on
understanding technology in organizations and homes. His research has been published in
leading information systems, organizational behavior, and psychology journals. He has served on
or is currently serving on the editorial boards of MIS Quarterly, Information Systems Research,
Journal of the AIS, and Decision Sciences.
Hillol Bala
Hillol Bala will start as an assistant professor of information systems at the Kelley School of
Business, Indiana University, Bloomington. He is expected to complete his PhD in Information
Systems at the Walton College of Business, University of Arkansas in 2008. He received M.B.A.
and M.S. degrees from Texas Tech University. His research interests are IT-enabled business
process change and management, post-adoption IT use and impact, and strategic use of IT in
health care. His research papers have been accepted for publication or published in MIS
Quarterly, Information Systems Research, Communications of the ACM, MIS Quarterly
Executive, and The Information Society.
... (2003) аccordingly. The similаrity of both models is exаmined bаsed on the cross-over effect (Venkаtesh & Bаlа, 2008). The differences аre: UTАUT considers moderаtors аs аn integrаting pаrt of the whole structure; meаnwhile TАM2 does not involve demogrаphic vаriаbles (i.e., аge аnd gender) . ...
...  Section D consists of eight questions (аdopted from the study of Venkаtesh & Bаlа (2008)) thаt аre relаted to the fаrmers' cognitive processes (thinking, feeling, etc.) related to their intention to аdopt аnd use e-commerce. The questions on Behаvioural ...
... Attitudes represent a person's disposition to something. Various studies that include studies by Venkatesh and Bala (2008) and Teo et al. (2008) found that the success of technology adoption and actual use by teachers in schools depends to a large extent on their attitudes towards ICT, since attitudes, whether negative or positive, define how a person responds to ICT. Confirming the importance of attitudes on the behavioural intentions of teachers to adopt and actually use ICT, a study by De-Graft (2018) found that positive attitudes towards ICT are critical to the successful adoption and use of ICT by teachers in their teaching. ...
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We are very happy to publish this issue of the International Journal of Learning, Teaching and Educational Research. The International Journal of Learning, Teaching and Educational Research is a peer-reviewed open-access journal committed to publishing high-quality articles in the field of education. Submissions may include full-length articles, case studies and innovative solutions to problems faced by students, educators and directors of educational organisations. To learn more about this journal, please visit the website http://www.ijlter.org. We are grateful to the editor-in-chief, members of the Editorial Board and the reviewers for accepting only high quality articles in this issue. We seize this opportunity to thank them for their great collaboration. The Editorial Board is composed of renowned people from across the world. Each paper is reviewed by at least two blind reviewers. We will endeavour to ensure the reputation and quality of this journal with this issue.
... Due to the lack of study conducted on the subject, we investigated, within our project, the use of Assistive platforms among Arabic-Speaking users. Our study was aimed at determining the availability and the accessibility to assistive platforms in the Arab world throw the framework of Technology Acceptance Model (TAM) 3 (11). ...
Article
Background: The Intel Assistive Context-Aware Toolkit (ACAT) is the highly configurable platform used by Dr. Stephen Hawking to communicate with his environment. After being released freely to the public, we, at the Embedded Systems Laboratory - UBMA, have been working on integrating the Arabic language on the different packages of the platform in order to make it accessible for disabled people from Arabic countries and decrease their communication limitations. Objective: This subproject concerns the Arabic Text-to-speech engine implementation and comes as a final step toward the full integration of the Arabic language into Intel ACAT assistive platform. Methods: The text to speech conversions was integrated by implementing a mapping between the Arabic words and their phonetic spelling using Microsoft Text-To-Speech Synthesis on Intel ACAT modules and extensions. A full compilation was then executed and tested gathering all the modules and the features of the platform. Results: Over this final integration step (which is freely accessible and open sourced for the public), people with severe disabilities from Arabic-speaking countries will have fully access to all the features of the ACAT platform and will be able communicate and interact easily with their computers. Conclusion: The Arabic language Text-to-speech engine integration on 'Intel ACAT' Assistive Platform is the final milestone of our project toward making the platform fully accessible for Arabic-Speaking users and comes after our previous integrations of the Arabic language into the keyboard, the intelligent predictive text engine and all panels and interfaces of the platform.
... Indicative examples include the work from: a) Liang and Yeh (46), who replaced the PU with the Perceived Entertainment, in an effort to understand if the users have different feelings when interacting with smartphone-based games across different settings and locations; b) Kim et al. (47), who added the construct of Perceived Value, to explore the system/service quality on users' beliefs of hospitality industry information management systems; c) Morosan (48), who added the construct of Perceived Innovation, when using TAM for exploring biometric systems utilization by hotels; d) Lee and Wan (49), who augmented TAM with the constructs of Functional Confidence and Familiarity for acceptance of airline e-ticketing services acceptance by travelers. These works show potential for TAM evolution; one of its referenced versions is TAM 3, which contemplates a comprehensive nomological network of the determinants of IT adoption and use by individuals (50). TAM has been tested and validated numerous times, and as Benbasat and Barki pointed out (51), TAM is considered as one of the most influential theories in IS. ...
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The ubiquitous nature of smartphone ownership, its broad application and usage, along with its interactive delivery of timely feedback are appealing for health-related behavior change interventions via mobile apps. However, users' perspectives about such apps are vital in better bridging the gap between their design intention and effective practical usage. In this vein, a modified technology acceptance model (mTAM) is proposed here, to explain the relationship between users' perspectives when using an AI-based smartphone app for personalized nutrition and healthy living, namely, PROTEIN, and the mTAM constructs toward behavior change in their nutrition and physical activity habits. In particular, online survey data from 85 users of the PROTEIN app within a period of 2 months were subjected to confirmatory factor analysis (CFA) and regression analysis (RA) to reveal the relationship of the mTAM constructs, i.e., perceived usefulness (PU), perceived ease of use (PEoU), perceived novelty (PN), perceived personalization (PP), usage attitude (UA), and usage intention (UI) with the users' behavior change (BC), as expressed via the acceptance/rejection of six related hypotheses (H1–H6), respectively. The resulted CFA-related parameters, i.e., factor loading (FL) with the related p-value, average variance extracted (AVE), and composite reliability (CR), along with the RA results, have shown that all hypotheses H1–H6 can be accepted (p < 0.001). In particular, it was found that, in all cases, FL > 0.5, CR > 0.7, AVE > 0.5, indicating that the items/constructs within the mTAM framework have good convergent validity. Moreover, the adjusted coefficient of determination (R2) was found within the range of 0.224–0.732, justifying the positive effect of PU, PEoU, PN, and PP on the UA, that in turn positively affects the UI, leading to the BC. Additionally, using a hierarchical RA, a significant change in the prediction of BC from UA when the UI is used as a mediating variable was identified. The explored mTAM framework provides the means for explaining the role of each construct in the functionality of the PROTEIN app as a supportive tool for the users to improve their healthy living by adopting behavior change in their dietary and physical activity habits. The findings herein offer insights and references for formulating new strategies and policies to improve the collaboration among app designers, developers, behavior scientists, nutritionists, physical activity/exercise physiology experts, and marketing experts for app design/development toward behavior change.
... According to the TAM (Davis, 1985;Venkatesh & Bala, 2008), the intention to use a system is influenced by the constructs Perceived Usefulness and Perceived Ease of Use (Table 1 for definitions). According to the UTAUT, the behavioral intention to use information technology is influenced by Performance Expectancy, Effort Expectancy, Social Influence and Facilitating conditions; see Table 1 for definitions) which are moderated by gender, age, experience, and voluntariness of use (Venkatesh et al., 2003). ...
Article
More and more technical systems enter the vehicle impacting drivers’ experiences. In the human-centered design, an understanding of influencing factors for acceptance and usage is crucial to align in-vehicle technology with the user needs. Addressing the underlying psychological processes, this work modelled drivers’ usage intentions with motivational regulations (SDT), the TAM, and the UTAUT. An online study with 319 German drivers was conducted examining drivers’ positive or negative experiences with assistance and infotainment systems in the vehicle. In linear regressions, the TAM and UTAUT predicted the acceptance equally for assistance and navigation systems. Amotivation, identified regulation, and intrinsic regulation enhanced the prediction of usage intentions by 3.0–15.4% in addition to the UTAUT variables revealing the additional benefit of incorporating the motivational perspective into the modeling of in-vehicle technology acceptance. Future research and practitioners can build upon this theoretical basis and recommendations on improving motivation and well-being.
... At this stage, (1) object acceptance does not need to monitor the road continuously, but must be ready to recover control of the vehicle at any time; (L4) L3 + the vehicle is capable of performing a safety maneuver (for example stop alone) if after a request for manual recovery the driver has not taken control; and (L5) Fully-automated driving with no need for a driver. comes from the comparison between the current situation and the future benefits brought about by the new technology (Bobillier Chaumon and Dubois 2009) and (2) this acceptance is measured by the intention to use the new object, which is the direct determinant of real use (for example, Venkatesh and Bala 2008;Venkatesh et al. 2003). Throughout the rest of the manuscript, we will use the term 'acceptance' for ease of reading. ...
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Autonomous vehicles (AVs) will profoundly modify our travel habits. The collective impact of AVs will differ according to the autonomous mode choice. In this paper, we apply a simultaneous-equation model to a database from an original 2017 survey of French mobility users to analyze their acceptance of two forms of autonomous transport mode: autonomous shuttles and robotaxis (N=3,297). Our results show that the intention to use autonomous shuttles is on average greater than robotaxis. Gender and age influence autonomous mode choice, as well as the current transport mode. In addition, location and urban representations play a central role.
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This article in the journal Gruppe. Interaktion. Organisation. introduces a model that provides an overview and orientation for science and practice regarding robots in elderly care. Aging societies and the lack of professionals working in elderly care put strain on the care sector in many countries worldwide. Robots can be a possible support for caregivers and assistance for people in need of care. However, their (future) usage comes along with various challenges and currently there are only few examples of use in practice. The data of the developed holistic triple-layered shell model SeRoNu (Service Robots in Nursing Homes) is based on three conducted studies: (I) A work analysis (HTO-Approach; Strohm and Ulich 1997), (II) future workshops (Jungk and Müllert 1989) and (III) expert interviews. Social robot Pepper is used as an example of application, as the model offers a framework for different service robots. The article illustrates the influencing factors and the diversity of robotic solutions to the care crisis. As a result, a multi-professional approach is required as the different aspects need to be considered individually.
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Behavioral intentions often have been used as a surrogate for actual behavior in choice models and to reflect the impact of marketing variables. The Fishbein behavioral intentions model posits two determinants of behavioral intentions: a personal or attitudinal component and a social influence or normative component. The authors use an experimental methodology to examine aspects of this model's construct validity. Certain operational problems are identified and related to underlying conceptual difficulties in separating these two components.
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The article focuses on the development of a theory. A discussion is presented about steps involved in developing a theory, such as seeing which factors logically should be considered as part of the explanation of the social or individual phenomena of interest. The authors assert that authors developing theories are considering these factors, they should err in favor of including too many factors, recognizing that over time their ideas will be refined. The article presents information about the importance of sensitivity to the competing virtues of parsimony and comprehensiveness.